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32nd Annual ACM Symposium on Applied Computing, SAC 2017

作者： Fitzgerald, Tadhg O'Sullivan, Barry Insight Centre for Data Analytics Department of Computer Science University College Cork Ireland

ISBN: (纸本)9781450344869

algorithm configuration has been repeatedly shown to have a large impact on improving the performance of solvers. Realtime algorithm configurators, such as ReACTR, are able to tune a solver online without incurring costly offine training. In order to do this ReACTR adopts a one-pass methodology where each instance in a stream of instances to be solved is considered only as it arrives. As such, the order in which instances are visited can affect the quality of the tuned parameters and, as a result, the solving time. ReACTR uses a selection procedure to choose multiple configurations to run from a set of possible candidates. These configurations are then run in parallel on each instance. A winner, which solves the problem quickest, or achieves the objective value, emerges. The information on this winner is then fed back into a ranking system which is used as part of the selection procedure and the cycle continues for all future instances. This paper explores both the effect of the instance ordering on configuration performance and which candidate selection policy is most effective in each case. Copyright 2017 ACM.

关键词： data streams instance ordering algorithm configuration

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Online Matching of algorithm configuration

Online Matching of Algorithm Configuration

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作者： Daniel T. G. Mocking Vrije Universiteit Amsterdam

学位级别：硕士

We propose and test a framework for the algorithm configuration problem. The framework utilizes a historical instance set in order to provide a panel of high-quality configurations which can be applied directly to new, previously unseen, instances. It is composed of three phases: clustering of historical instances, generation of a configuration for each cluster, and matching a new instance to a cluster and its corresponding configuration. We apply the framework to a VRP solver and compare the quality of solutions to new instances when using two methods for clustering: feature-based and solution-based. We conclude that in three out of the four cases we investigated, the clusterings that are created are of higher quality when based on instance solutions, instead of instance features. When applying the complete framework, including the matching, the results are less decisive. In one case, using feature-based clustering clearly results in better solutions. In another, both methods give similar results. In the remaining two cases solution-based clustering appears to perform better, but more data are required to substantiate this claim. We suspect that further improvement of the quality of the matching can lead to better results, especially for solution-based clusterings.

关键词： algorithm configuration parameter tuning clustering

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MaxSAT by improved instance-specific algorithm configuration

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ARTIFICIAL INTELLIGENCE 2016年 235卷 26-39页

作者： Ansotegui, Carlos Gabas, Joel Malitsky, Yuri Sellmann, Meinolf Univ Lleida Dept Comp Sci Lleida Spain IBM Watson Res Ctr Yorktown Hts NY 10598 USA

Our objective is to boost the state-of-the-art performance in MaxSAT solving. To this end, we employ the instance-specific algorithm configurator ISAC, and improve it with the latest in portfolio technology. Experimental results on SAT show that this combination marks a significant step forward in our ability to tune algorithms instance-specifically. We then apply the new methodology to a number of MaxSAT problem domains and show that the resulting solvers consistently outperform the best existing solvers on the respective problem families. In fact, the solvers presented here were independently evaluated at the 2013 and 2014 MaxSAT Evaluations where they won several categories. (C) 2016 Elsevier B.V. All rights reserved.

关键词： algorithm configuration algorithm selection MaxSAT

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Feature Based algorithm configuration: A Case Study with Differential Evolution

Feature Based Algorithm Configuration: A Case Study with Dif...

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14th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Belkhir, Nacim Dreo, Johann Saveant, Pierre Schoenauer, Marc Thales Res Technol Palaiseau France Inria Saclay Ile de France TAO Orsay France

ISBN: (纸本)9783319458236;9783319458229

algorithm configuration is still an intricate problem especially in the continuous black box optimization domain. This paper empirically investigates the relationship between continuous problem features (measuring different problem characteristics) and the best parameter configuration of a given stochastic algorithm over a bench of test functions - namely here, the original version of Differential Evolution over the BBOB test bench. This is achieved by learning an empirical performance model from the problem features and the algorithm parameters. This performance model can then be used to compute an empirical optimal parameter configuration from features values. The results show that reasonable performance models can indeed be learned, resulting in a better parameter configuration than a static parameter setting optimized for robustness over the test bench.

关键词： Empirical study Black-box continuous optimization Problem features algorithm configuration Empirical Performance Model Differential Evolution

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MO-ParamILS: A Multi-objective Automatic algorithm configuration Framework 10th

MO-ParamILS: A Multi-objective Automatic Algorithm Configura...

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10th International Conference on Learning and Intelligent Optimization (LION)

作者： Blot, Aymeric Hoos, Holger H. Jourdan, Laetitia Kessaci-Marmion, Marie-Eleonore Trautmann, Heike Univ Lille Inria CNRS UMR CRIStAL 9189 Lille France Ecole Normale Super Rennes Rennes France Univ British Columbia Vancouver BC Canada Univ Munster Munster Germany

ISBN: (纸本)9783319503493;9783319503486

Automated algorithm configuration procedures play an increasingly important role in the development and application of algorithms for a wide range of computationally challenging problems. Until very recently, these configuration procedures were limited to optimising a single performance objective, such as the running time or solution quality achieved by the algorithm being configured. However, in many applications there is more than one performance objective of interest. This gives rise to the multi-objective automatic algorithm configuration problem, which involves finding a Pareto set of configurations of a given target algorithm that characterises trade-offs between multiple performance objectives. In this work, we introduce MO-ParamILS, a multiobjective extension of the state-of-the-art single-objective algorithm configuration framework ParamILS, and demonstrate that it produces good results on several challenging bi-objective algorithm configuration scenarios compared to a base-line obtained from using a state-of-the-art single-objective algorithm configurator.

关键词： algorithm configuration Parameter tuning Multi-objective optimisation Local search algorithms

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A filtering method for algorithm configuration based on consistency techniques

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KNOWLEDGE-BASED SYSTEMS 2014年第Apr.期60卷 73-81页

作者： Araya, Ignacio Riff, Maria-Cristina Univ Tecn Federico Santa Maria Dept Informat V Reg Chile

Heuristic based algorithms are typically constructed following an iterative process in which the designer gradually introduces or modifies components or strategies whose performance is then tested by empirical evaluation on one or more sets of benchmark problems. This process often starts with some generic or broadly applicable problem solving method (e.g., metaheuristics, backtracking search), a new algorithmic idea or even an algorithm suggested by theoretical considerations. Then, through an iterative process, various combinations of components, methods and strategies are implemented/improved and tested. Even experienced designers often have to spend substantial amounts of time exploring and experimenting with different alternatives before obtaining an effective algorithm for a given problem. In this work, we are interested in assisting the designer in this task. Considering that components, methods and strategies are generally associated with parameters and parameter values, we propose a method able to detect, through a fine-tuning process, ineffective and redundant components/strategies of an algorithm. The approach is a model-free method and applies simple consistency techniques in order to discard values from the domain of the parameters. We validate our approach with two algorithms for solving SAT and MIP problems. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Parameter tuning Constraint satisfaction problems Consistency techniques algorithm configuration algorithm design

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Automatic MILP solver configuration by learning problem similarities

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ANNALS OF OPERATIONS RESEARCH 2024年第1-2期339卷 909-936页

作者： Hosny, Abdelrahman Reda, Sherief Brown Univ Dept Comp Sci Providence RI 02912 USA Brown Univ Sch Engn Providence RI 02912 USA

A large number of real-world optimization problems can be formulated as Mixed Integer Linear Programs (MILP). MILP solvers expose numerous configuration parameters to control their internal algorithms. Solutions, and their associated costs or runtimes, are significantly affected by the choice of the configuration parameters, even when problem instances have the same number of decision variables and constraints. On one hand, using the default solver configuration leads to suboptimal solutions. On the other hand, searching and evaluating a large number of configurations for every problem instance is time-consuming and, in some cases, infeasible. In this study, we aim to predict configuration parameters for unseen problem instances that yield lower-cost solutions without the time overhead of searching-and-evaluating configurations at the solving time. Toward that goal, we first investigate the cost correlation of MILP problem instances that come from the same distribution when solved using different configurations. We show that instances that have similar costs using one solver configuration also have similar costs using another solver configuration in the same runtime environment. After that, we present a methodology based on Deep Metric Learning to learn MILP similarities that correlate with their final solutions' costs. At inference time, given a new problem instance, it is first projected into the learned metric space using the trained model, and configuration parameters are instantly predicted using previously-explored configurations from the nearest neighbor instance in the learned embedding space. Empirical results on real-world problem benchmarks show that our method predicts configuration parameters that improve solutions' costs by up to 38% compared to existing approaches.

关键词： Mixed integer linear programming algorithm configuration Metric learning Deep learning

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Voting enabled dynamic constraint handling portfolios for evolutionary multi/many-objective optimization

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INFORMATION SCIENCES 2025年 694卷

作者： Zhou, Jiajun Liu, Zhao Li, Yongxiang Lu, Chao Gao, Liang China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Guizhou Univ Engn Sci Sch Mech Engn Bijie 551700 Peoples R China Huazhong Univ Sci & Technol State Key Lab Intelligent Mfg Equipment & Technol Wuhan 430074 Peoples R China

Constraint handling technique (CHT) plays a key role in handling constrained multi-objective optimization problems (CMOPs). In general, CHTs are associated with their own pros and cons under different scenarios, giving rise to an inherent risk in the selection of appropriate CHTs. To take advantages of diverse CHTs, we design a novel competitive co-evolution scheme for CHT portfolios in which multiple complementary CHTs are synergized via a single-population competitive ensemble framework. A self-organizing voting strategy is developed to allocate restrictive resources to constituent CHTs adaptively according to the feedbacks of CHTs as the search proceeds. In addition, an archive update strategy is designed to facilitate effective usage of potential solutions and achieve better compromise between convergence and diversity. Then the concrete algorithm, namely, VP-CMOEA, is presented. We have comprehensively evaluated our proposal on diverse benchmark functions and real-world applications. Empirical results demonstrate the better generality of VP-CMOEA compared with state-of-the-art peers.

关键词： algorithm configuration Multi-objective optimization Evolutionary algorithm Constraint handling method Dynamic resource allocation

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Dancing to the State of the Art? How Candidate Lists Influence LKH for Solving the Traveling Salesperson Problem 18th

Dancing to the State of the Art? How Candidate Lists Influen...

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18th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Heins, Jonathan Schaepermeier, Lennart Kerschke, Pascal Whitley, Darrell Tech Univ Dresden Big Data Analyt Transportat Dresden Germany ScaDS AI Dresden Leipzig Dresden Germany Colorado State Univ Dept Comp Sci Ft Collins CO USA

ISBN: (纸本)9783031700545;9783031700552

Solving the Traveling Salesperson Problem (TSP) remains a persistent challenge, despite its fundamental role in numerous generalized applications in modern contexts. Heuristic solvers address the demand for finding high-quality solutions efficiently. Among these solvers, the Lin-Kernighan-Helsgaun (LKH) heuristic stands out, as it complements the performance of genetic algorithms across a diverse range of problem instances. However, frequent timeouts on challenging instances hinder the practical applicability of the solver. Within this work, we investigate a previously overlooked factor contributing to many timeouts: The use of a fixed candidate set based on a tree structure. Our investigations reveal that candidate sets based on Hamiltonian circuits contain more optimal edges. We thus propose to integrate this promising initialization strategy, in the form of POPMUSIC, within an efficient restart version of LKH. As confirmed by our experimental studies, this refined TSP heuristic is much more efficient - causing fewer timeouts and improving the performance (in terms of penalized average runtime) by an order of magnitude - and thereby challenges the state of the art in TSP solving.

关键词： Traveling Salesperson Problem Heuristic Search Problem Hardness algorithm configuration Benchmarking

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Instance selection for configuration performance comparison

Instance selection for configuration performance comparison

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ECML/PKDD Workshop on Meta-Knowledge Transfer

作者： Anastacio, Marie Matricon, Theo Hoos, Holger H. Leiden Univ Leiden Inst Adv Comp Sci Leiden Netherlands Univ Bordeaux CNRS UMR 5800 LaBRI F-33400 Talence France Rhein Westfal TH Aachen Aachen Germany Univ British Columbia Vancouver BC Canada

Comparing the performance of two configurations of a given algorithm plays a critical role in algorithm configuration and performance optimisation, be it automated or manual, and requires substantial computational resources. Time is often wasted on less promising configurations but also on instances that require a long running time regardless of the configuration. Prior work has shown that by running an algorithm on carefully selected instances, the time required to accurately decide the better of two given algorithms can be significantly reduced. In this work, we explore ways to apply a similar selection process to compare two configurations of the same algorithm. We adapted four selection methods from the literature to work with the performance model used in model-based configurators and evaluated them on six benchmarks. Our experimental evaluation shows that, depending on the problem instances and their running time distribution, a decision can be reached 5 to 3000 times faster than with random sampling, the method used in current state-of-the-art configurators.

关键词： instance selection running time optimisation algorithm configuration

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